Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis

This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtai...

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Main Author: Chinedu I. Ossai
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2018-06-01
Series:International Journal of Prognostics and Health Management
Subjects:
Online Access:https://papers.phmsociety.org/index.php/ijphm/article/view/2703
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spelling doaj-b8d18d269fa34431938fc86968d66b222021-07-02T19:09:47ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482018-06-0192doi:10.36001/ijphm.2018.v9i2.2703Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative AnalysisChinedu I. Ossai0School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes, GPO Box 2471 Adelaide SA 5001, AustraliaThis study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated.https://papers.phmsociety.org/index.php/ijphm/article/view/2703charge capacity decaydegradation modelnonlinear mixed effect modelslithium-ion batteryreliabilityuncertainty
collection DOAJ
language English
format Article
sources DOAJ
author Chinedu I. Ossai
spellingShingle Chinedu I. Ossai
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
International Journal of Prognostics and Health Management
charge capacity decay
degradation model
nonlinear mixed effect models
lithium-ion battery
reliability
uncertainty
author_facet Chinedu I. Ossai
author_sort Chinedu I. Ossai
title Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
title_short Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
title_full Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
title_fullStr Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
title_full_unstemmed Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
title_sort prognostics health estimation of lithium-ion batteries in charge-decay estimation uncertainties – a comparative analysis
publisher The Prognostics and Health Management Society
series International Journal of Prognostics and Health Management
issn 2153-2648
2153-2648
publishDate 2018-06-01
description This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated.
topic charge capacity decay
degradation model
nonlinear mixed effect models
lithium-ion battery
reliability
uncertainty
url https://papers.phmsociety.org/index.php/ijphm/article/view/2703
work_keys_str_mv AT chineduiossai prognosticshealthestimationoflithiumionbatteriesinchargedecayestimationuncertaintiesacomparativeanalysis
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